RNA-Seq: TFA analysis of SC Controls

Libraries required
Data
TFA analysis
Results
SessionInfo

Libraries required

library(plgINS)
library(plotly)
library(UpSetR)
library(RColorBrewer)
library(grid)
library(gridExtra)
library(DT)

Data

load("input/dea_SC_Controls.DEA.RData")
load("input/voom_EList_SC_Controls.RData")
load("input/SC_controls_rnaseq_salmon.tds.RData")
data(regulon.curated.mm)

TFA analysis

Transcription Factor Binding Affinity analysis is performed using Virtual Inference of Protein-activity by Enriched Regulon analysis (VIPER) algorithm.

Regulon is a group of genes that are regulated as a unit, generally controlled by the same regulatory gene that expresses a protein acting as a repressor or activator.

More details: 10.1038/ng.3593

Function to show table

make_DT <- function(tab) {
  df <- data.frame(TF = rownames(tab), tab, check.names = F, stringsAsFactors = F)
  DT::datatable(
    df,
    rownames = F,
    filter = "top", extensions = c("Buttons", "ColReorder"), options = list(
      autoWidth = TRUE,
      columnDefs = list(list(
        # targets = 12:13,
        render = JS(
          "function(data, type, row, meta) {",
          "return type === 'display' && data.length > 8 ?",
          "'<span title=\"' + data + '\">' + data.substr(0, 8) + '...</span>' : data;",
          "}"
        )
      )),
      pageLength = 10,
      buttons = c("copy", "csv", "excel", "pdf", "print"),
      colReorder = list(realtime = FALSE),
      dom = "fltBip"
    )
  )
}

Filters a viper regulon by likelihood

regulon.mm10 <- regulon.filter(regulon = regulon.curated.mm, min.likelihood = 0.15)

TFA analysis

Function for running analysis

runTFA <- function(voomEL, dea, pData, pDataCol, group1, group2, regulon) {
  s1 <- rownames(pData[pData[, pDataCol] == group1, ])
  s2 <- rownames(pData[pData[, pDataCol] == group2, ])
  se <- voomEList$E[, c(s2, s1)]
  pData <- pData[colnames(se), ]
  design <- model.matrix(~ 0 + Group, pData)
  tfa <- TFA(se = se, dea = dea, design = design, regulon = regulon)
  colData(tfa) <- DataFrame(pData)
  return(tfa)
}

All regulons

tfa.all.pnd8_pnd15 <- runTFA(
  voomEL = voomEList, dea = dea.list$`PND8 vs PND15`,
  pData = salmon@phenoData, pDataCol = "Group",
  group1 = "PND8", group2 = "PND15",
  regulon = regulon.curated.mm
)

tfa.all.pnd15_adult <- runTFA(
  voomEL = voomEList, dea = dea.list$`PND15 vs Adult`,
  pData = salmon@phenoData, pDataCol = "Group",
  group1 = "PND15", group2 = "Adult",
  regulon = regulon.curated.mm
)

Filtered regulons

tfa.filt.pnd8_pnd15 <- runTFA(
  voomEL = voomEList, dea = dea.list$`PND8 vs PND15`,
  pData = salmon@phenoData, pDataCol = "Group",
  group1 = "PND8", group2 = "PND15",
  regulon = regulon.mm10
)

tfa.filt.pnd15_adult <- runTFA(
  voomEL = voomEList, dea = dea.list$`PND15 vs Adult`,
  pData = salmon@phenoData, pDataCol = "Group",
  group1 = "PND15", group2 = "Adult",
  regulon = regulon.mm10
)

Results

Volcano plots

All regulons

p <- subplot(plotTFA(tfa.all.pnd8_pnd15) %>%
  layout(
    xaxis = list(range = c(-10, 10)), yaxis = list(range = c(-1, 32)),
    font = list(size = 17)
  ),
plotTFA(tfa.all.pnd15_adult) %>%
  layout(
    xaxis = list(range = c(-10, 10)), yaxis = list(range = c(-1, 32)),
    font = list(size = 17)
  ),
nrows = 2, shareX = T, shareY = T, titleX = T, titleY = T
)

p %>% layout(title = "", annotations = list(
  list(
    x = 0.5, y = 1.00, text = "PND8 vs PND15", showarrow = F, xref = "paper", yref = "paper",
    font = list(color = "red", family = "Arial", size = 20)
  ),
  list(
    x = 0.5, y = 0.5, text = "PND15 vs Adult", showarrow = F, xref = "paper", yref = "paper",
    font = list(color = "red", family = "Arial", size = 20)
  )
), showlegend = FALSE)

Filtered regulons

p <- subplot(plotTFA(tfa.filt.pnd8_pnd15) %>%
  layout(
    xaxis = list(range = c(-10, 10)), yaxis = list(range = c(-1, 32)),
    font = list(size = 17)
  ),
plotTFA(tfa.filt.pnd15_adult) %>%
  layout(
    xaxis = list(range = c(-10, 10)), yaxis = list(range = c(-1, 32)),
    font = list(size = 17)
  ),
nrows = 2, shareX = T, shareY = T, titleX = T, titleY = T
)

p %>% layout(title = "", annotations = list(
  list(
    x = 0.5, y = 1.04, text = "PND8 vs PND15", showarrow = F, xref = "paper", yref = "paper",
    font = list(color = "red", family = "Arial", size = 20)
  ),
  list(
    x = 0.5, y = 0.5, text = "PND15 vs Adult", showarrow = F, xref = "paper", yref = "paper",
    font = list(color = "red", family = "Arial", size = 20)
  )
), showlegend = FALSE)

UpSetPlot

All regulons

tfa.all.union <- Reduce(
  union,
  list(
    rownames(rowData(tfa.all.pnd8_pnd15)[rowData(tfa.all.pnd8_pnd15)[, "activity.FDR"] <= 0.05, ]),
    rownames(rowData(tfa.all.pnd15_adult)[rowData(tfa.all.pnd15_adult)[, "activity.FDR"] <= 0.05, ])
  )
)

tfa.all.upset <- data.frame(
  TF = tfa.all.union, `PND8 vs PND15` = 0, `PND15 vs Adult` = 0,
  check.names = F, stringsAsFactors = F
)
rownames(tfa.all.upset) <- tfa.all.upset$TF


tfa.all.upset[rownames(rowData(tfa.all.pnd8_pnd15)[rowData(tfa.all.pnd8_pnd15)[, "activity.FDR"] <= 0.05, ]), 2] <- 1

tfa.all.upset[rownames(rowData(tfa.all.pnd15_adult)[rowData(tfa.all.pnd15_adult)[, "activity.FDR"] <= 0.05, ]), 3] <- 1

col <- brewer.pal(7, "Set1")


upset(tfa.all.upset[, 2:3],
  point.size = 5, sets.bar.color = col[1:2], matrix.color = col[5],
  order.by = "freq", set_size.numbers_size = T, text.scale = c(2.5, 3, 2.5, 2.5, 2.5, 3)
)

grid.edit("arrange", name = "UpSet")

vp <- grid.grab()

grid.arrange(
  grobs = list(
    vp
  ),
  top = "TFA in SC at different stages of development (FDR <= 0.05)",
  cols = 1
)

grid.arrange(
  grobs = list(
    vp
  ),
  top = "TFA in SC at different stages of development (FDR <= 0.05)",
  cols = 1
)

Filtered regulons

tfa.filt.union <- Reduce(
  union,
  list(
    rownames(rowData(tfa.filt.pnd8_pnd15)[rowData(tfa.filt.pnd8_pnd15)[, "activity.FDR"] <= 0.05, ]),
    rownames(rowData(tfa.filt.pnd15_adult)[rowData(tfa.filt.pnd15_adult)[, "activity.FDR"] <= 0.05, ])
  )
)

tfa.filt.upset <- data.frame(
  TF = tfa.filt.union, `PND8 vs PND15` = 0, `PND15 vs Adult` = 0,
  check.names = F, stringsAsFactors = F
)
rownames(tfa.filt.upset) <- tfa.filt.upset$TF


tfa.filt.upset[rownames(rowData(tfa.filt.pnd8_pnd15)[rowData(tfa.filt.pnd8_pnd15)[, "activity.FDR"] <= 0.05, ]), 2] <- 1

tfa.filt.upset[rownames(rowData(tfa.filt.pnd15_adult)[rowData(tfa.filt.pnd15_adult)[, "activity.FDR"] <= 0.05, ]), 3] <- 1

col <- brewer.pal(7, "Set1")


upset(tfa.filt.upset[, 2:3],
  point.size = 5, sets.bar.color = col[1:2], matrix.color = col[5],
  order.by = "freq", set_size.numbers_size = T, text.scale = c(2.5, 3, 2.5, 2.5, 2.5, 3)
)

grid.edit("arrange", name = "UpSet")

vp <- grid.grab()

grid.arrange(
  grobs = list(
    vp
  ),
  top = "TFA in SC at different stages of development (FDR <= 0.05)",
  cols = 1
)

grid.arrange(
  grobs = list(
    vp
  ),
  top = "TFA in SC at different stages of development (FDR <= 0.05)",
  cols = 1
)

Heatmaps of TBA

Data prep

All regulons

tfa.all.pnd8_pnd15 <- tfa.all.pnd8_pnd15[, c(
  grep(pattern = "PND8", x = colnames(tfa.all.pnd8_pnd15), value = T),
  grep(pattern = "PND15", x = colnames(tfa.all.pnd8_pnd15), value = T)
)]

# rowData(tfa.all$`PND8 vs PND15`) <- rowData(tfa.all$`PND8 vs PND15`)[order(rowData(tfa.all$`PND8 vs PND15`)[, "activity.FDR"]), ]

tfa.all.pnd15_adult <- tfa.all.pnd15_adult[, c(
  grep(pattern = "PND15", x = colnames(tfa.all.pnd15_adult), value = T),
  grep(pattern = "Adult", x = colnames(tfa.all.pnd15_adult), value = T)
)]

# rowData(tfa$`PND15 vs Adult`) <- rowData(tfa$`PND15 vs Adult`)[order(rowData(tfa$`PND15 vs Adult`)[, 11]), ]

Filtered regulons

tfa.filt.pnd8_pnd15 <- tfa.filt.pnd8_pnd15[, c(
  grep(pattern = "PND8", x = colnames(tfa.filt.pnd8_pnd15), value = ),
  grep(pattern = "PND15", x = colnames(tfa.filt.pnd8_pnd15), value = )
)]

# rowData(tfa$`PND8 vs PND15`) <- rowData(tfa$`PND8 vs PND15`)[order(rowData(tfa$`PND8 vs PND15`)[, 11]), ]


tfa.filt.pnd15_adult <- tfa.filt.pnd15_adult[, c(
  grep(pattern = "PND15", x = colnames(tfa.filt.pnd15_adult), value = ),
  grep(pattern = "Adult", x = colnames(tfa.filt.pnd15_adult), value = )
)]

# rowData(tfa$`PND8 vs Adult`) <- rowData(tfa$`PND8 vs Adult`)[order(rowData(tfa$`PND8 vs Adult`)[, 11]), ]

Heatmaps

All regulons

PND8 vs PND15

sehm(
  hmcols = viridis::viridis(1000), se = tfa.filt.pnd8_pnd15[1:20, ],
  anno_columns = c("Group", "Cage.ID", "Batch"), scale = "row",
  anno_rows = c("activity.logFC", "activity.FDR", "targetEnrichment", "targetEnrFDR")
)

## Registered S3 method overwritten by 'seriation':
##   method         from 
##   reorder.hclust gclus

PND15 vs Adult

sehm(
  hmcols = viridis::viridis(1000), se = tfa.all.pnd15_adult[1:20, ],
  anno_columns = c("Group", "Cage.ID", "Batch"), scale = "row",
  anno_rows = c("activity.logFC", "activity.FDR", "targetEnrichment", "targetEnrFDR")
)

Filtered regulons

PND8 vs PND15

sehm(
  hmcols = viridis::viridis(1000), se = tfa.filt.pnd8_pnd15[1:20, ],
  anno_columns = c("Group", "Cage.ID", "Batch"), scale = "row",
  anno_rows = c("activity.logFC", "activity.FDR", "targetEnrichment", "targetEnrFDR")
)

PND15 vs Adult

sehm(
  hmcols = viridis::viridis(1000), se = tfa.filt.pnd15_adult[1:20, ],
  anno_columns = c("Group", "Cage.ID", "Batch"), scale = "row",
  anno_rows = c("activity.logFC", "activity.FDR", "targetEnrichment", "targetEnrFDR")
)

Tables

All regulons

PND8 vs PND15

make_DT(tab = rowData(tfa.all.pnd8_pnd15))

PND15 vs Adult

make_DT(tab = rowData(tfa.all.pnd15_adult))

Filtered regulons

PND8 vs PND15

make_DT(tab = rowData(tfa.filt.pnd8_pnd15))

PND15 vs Adult

make_DT(tab = rowData(tfa.filt.pnd15_adult))

SessionInfo

devtools::session_info()

## ─ Session info ──────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.6.1 (2019-07-05)
##  os       Ubuntu 16.04.6 LTS          
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       Europe/Zurich               
##  date     2019-09-02                  
## 
## ─ Packages ──────────────────────────────────────────────────────────────
##  package              * version   date       lib source        
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##  sessioninfo            1.1.1     2018-11-05 [1] CRAN (R 3.6.1)
##  shiny                  1.3.2     2019-04-22 [1] CRAN (R 3.6.1)
##  SRAdb                  1.46.0    2019-05-02 [1] Bioconductor  
##  stringi                1.4.3     2019-03-12 [1] CRAN (R 3.6.1)
##  stringr                1.4.0     2019-02-10 [1] CRAN (R 3.6.1)
##  SummarizedExperiment * 1.14.1    2019-07-31 [1] Bioconductor  
##  survival               2.44-1.1  2019-04-01 [1] CRAN (R 3.6.1)
##  testthat               2.2.1     2019-07-25 [1] CRAN (R 3.6.1)
##  tibble                 2.1.3     2019-06-06 [1] CRAN (R 3.6.1)
##  tidyr                  0.8.3     2019-03-01 [1] CRAN (R 3.6.1)
##  tidyselect             0.2.5     2018-10-11 [1] CRAN (R 3.6.1)
##  TSP                    1.1-7     2019-05-22 [1] CRAN (R 3.6.1)
##  UpSetR               * 1.4.0     2019-05-22 [1] CRAN (R 3.6.1)
##  usethis                1.5.1     2019-07-04 [1] CRAN (R 3.6.1)
##  vctrs                  0.2.0     2019-07-05 [1] CRAN (R 3.6.1)
##  viper                * 1.18.1    2019-05-30 [1] Bioconductor  
##  viridis                0.5.1     2018-03-29 [1] CRAN (R 3.6.1)
##  viridisLite            0.3.0     2018-02-01 [1] CRAN (R 3.6.1)
##  withr                  2.1.2     2018-03-15 [1] CRAN (R 3.6.1)
##  xfun                   0.9       2019-08-21 [1] CRAN (R 3.6.1)
##  XML                    3.98-1.20 2019-06-06 [1] CRAN (R 3.6.1)
##  xml2                   1.2.2     2019-08-09 [1] CRAN (R 3.6.1)
##  xtable                 1.8-4     2019-04-21 [1] CRAN (R 3.6.1)
##  XVector                0.24.0    2019-05-02 [1] Bioconductor  
##  yaml                   2.2.0     2018-07-25 [1] CRAN (R 3.6.1)
##  zeallot                0.1.0     2018-01-28 [1] CRAN (R 3.6.1)
##  zlibbioc               1.30.0    2019-05-02 [1] Bioconductor  
## 
## [1] /home/ubuntu/R/x86_64-pc-linux-gnu-library/3.6
## [2] /usr/local/lib/R/site-library
## [3] /usr/lib/R/site-library
## [4] /usr/lib/R/library